code: def box_iou_xywh(box1: np.ndarray, box2: np.ndarray) -> float:
      """Calculate iou by xywh"""
  
      assert box1.shape[-1] == 4, "Box1 shape[-1] should be 4."
      assert box2.shape[-1] == 4, "Box2 shape[-1] should be 4."
  
      b1_x1, b1_x2 = (
          box1[:, 0] - box1[:, 2] / 2,
          box1[:, 0] + box1[:, 2] / 2,
      )
      b1_y1, b1_y2 = (
          box1[:, 1] - box1[:, 3] / 2,
          box1[:, 1] + box1[:, 3] / 2,
      )
      b2_x1, b2_x2 = (
          box2[:, 0] - box2[:, 2] / 2,
          box2[:, 0] + box2[:, 2] / 2,
      )
      b2_y1, b2_y2 = (
          box2[:, 1] - box2[:, 3] / 2,
          box2[:, 1] + box2[:, 3] / 2,
      )
  
      inter_x1 = np.maximum(b1_x1, b2_x1)
      inter_x2 = np.minimum(b1_x2, b2_x2)
      inter_y1 = np.maximum(b1_y1, b2_y1)
      inter_y2 = np.minimum(b1_y2, b2_y2)
      inter_w = inter_x2 - inter_x1
      inter_h = inter_y2 - inter_y1
      inter_w[inter_w < 0] = 0
      inter_h[inter_h < 0] = 0
  
      inter_area = inter_w * inter_h
      b1_area = (b1_x2 - b1_x1) * (b1_y2 - b1_y1)
      b2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1)
  
      return inter_area / (b1_area + b2_area - inter_area)
  
imports: import os
  from typing import Callable, Union, List, Tuple
  import cv2
  import paddle
  import PIL
  import numpy
  import paddle.nn.functional
  from scipy.sparse import csr_matrix
label_desc: Use image node_id 4 for calling a global function with example usage: box_iou_xywh(box1, box2) and returns: unknown